This study presents a straightforward method to teach robots to use tools. Teaching robots is crucial in quickly deploying and reconfiguring robots in next-generation factories. Conventional methods require third-party systems like wearable devices or complicated vision system to capture, analyse, and map human grasps, motion, and tool poses to robots. These systems assume lots of experience from their users. Unlike the conventional methods, this study does not involve learning human motion and skills. Instead, it only learns the object goal poses from the human user whilst employs regrasp planning to generate robot motion. The method is most suitable for a robot to learn the usage of electric tools that can be operated by simply switching on and off. The proposed method is validated using a dual-arm robot with hand-mounted cameras and several tools. Experimental results show that the proposed method is robust, feasible, and simple to teach robots. It can find a collision-free and kino-dynamic feasible grasp sequences and motion trajectories when the goal pose is reachable. The method allows the robot to automatically choose placements or handover considering the surrounding environment as intermediate states to change the pose of the tool and use tools following human demonstrations.
Purpose This paper aims to present a hierarchical motion planner for planning the manipulation motion to repose long and heavy objects considering external support surfaces. Design/methodology/approach The planner includes a task-level layer and a motion-level layer. This paper formulates the manipulation planning problem at the task level by considering grasp poses as nodes and object poses for edges. This paper considers regrasping and constrained in-hand slip (drooping) during building graphs and find mixed regrasping and drooping sequences by searching the graph. The generated sequences autonomously divide the object weight between the arm and the support surface and avoid configuration obstacles. Cartesian planning is used at the robot motion level to generate motions between adjacent critical grasp poses of the sequence found by the task-level layer. Findings Various experiments are carried out to examine the performance of the proposed planner. The results show improved capability of robot arms to manipulate long and heavy objects using the proposed planner. Originality/value The authors’ contribution is that they initially develop a graph-based planning system that reasons both in-hand and regrasp manipulation motion considering external supports. On one hand, the planner integrates regrasping and drooping to realize in-hand manipulation with external support. On the other hand, it switches states by releasing and regrasping objects when the object is in stably placed. The search graphs' nodes could be retrieved from remote cloud servers that provide a large amount of pre-annotated data to implement cyber intelligence.
Transferring humans while carrying loads on all types of terrains efficiently using compact means of transportation is still a challenge. This may be due to the unstructured areas humans need to move through, or due to traffic congestions in structured roads. Some compact-size, light-weight transportation systems have been developed but they provide only a partial solution to the stated problem. This paper presents the dynamics analysis and control of a novel all-terrains wearable vehicle, a new transportation means consisting of a lower extremity exoskeleton carrying two motorized wheels and two free wheels. On flat free ground, this novel system utilizes its wheels to travel fast and when faced with crowded traffic or unstructured area, the human just switches into walking mode. CAD models of the human and the wearable vehicle are developed, and a dynamic human walker is built in MSC ADAMS and is used for proving the feasibility the proposed wearable vehicle in its two modes of operation. PD controller with gravity compensation is designed to ensure that the wearable vehicle is tracking the human motion in the walking mode, and the results obtained show the effectiveness of the controller, the vehicle dynamics are also studied, and the critical acceleration safety margins are defined.
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